# Spatial Intention Maps for Multi-Agent Mobile Manipulation

arXiv: https://arxiv.org/abs/2103.12710 · HTML: https://mlsanity.com/paper/2103.12710/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 52 references that are in the map.
- First posted on arXiv 23 Mar 2021.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Value-Decomposition Networks For Cooperative Multi-Agent Learning](https://mlsanity.com/paper/1706.05296/) (2017)
- [Learning Multiagent Communication with Backpropagation](https://mlsanity.com/paper/1605.07736/) (2016)
- [Emergent Tool Use From Multi-Agent Autocurricula](https://mlsanity.com/paper/1909.07528/) (2019)
- [Human-level performance in first-person multiplayer games with population-based deep reinforcement learning](https://mlsanity.com/paper/1807.01281/) (2018)
- [Emergence of Grounded Compositional Language in Multi-Agent Populations](https://mlsanity.com/paper/1703.04908/) (2017)
- [Multiagent Cooperation and Competition with Deep Reinforcement Learning](https://mlsanity.com/paper/1511.08779/) (2015)
- [A Survey and Critique of Multiagent Deep Reinforcement Learning](https://mlsanity.com/paper/1810.05587/) (2018)
- [Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability](https://mlsanity.com/paper/1703.06182/) (2017)
- [Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1702.08887/) (2017)
- [Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real](https://mlsanity.com/paper/1908.05224/) (2019)
- [Spatial Action Maps for Mobile Manipulation](https://mlsanity.com/paper/2004.09141/) (2020)
- [Macro-Action-Based Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2004.08646/) (2020)
- [Multi-Agent Collaboration via Reward Attribution Decomposition](https://mlsanity.com/paper/2010.08531/) (2020)
- [Intent-aware Multi-agent Reinforcement Learning](https://mlsanity.com/paper/1803.02018/) (2018)
- [Learning Multi-Robot Decentralized Macro-Action-Based Policies via a Centralized Q-Net](https://mlsanity.com/paper/1909.08776/) (2019)
- [Model-based Reinforcement Learning for Decentralized Multiagent Rendezvous](https://mlsanity.com/paper/2003.06906/) (2020)

## Built on by (most cited first, 11 of 11)

- [Visual Language Maps for Robot Navigation](https://mlsanity.com/paper/2210.05714/) (2022)
- [VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects](https://mlsanity.com/paper/2106.14440/) (2021)
- [Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2408.09675/) (2024)
- [Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2209.10113/) (2022)
- [Multimodal Spatial Language Maps for Robot Navigation and Manipulation](https://mlsanity.com/paper/2506.06862/) (2025)
- [Decentralized Multi-Agent Reinforcement Learning with Global State Prediction](https://mlsanity.com/paper/2306.12926/) (2023)
- [Macro-Action-Based Multi-Agent/Robot Deep Reinforcement Learning under Partial Observability](https://mlsanity.com/paper/2209.10003/) (2022)
- [Intent-Aware Autonomous Driving: A Case Study on Highway Merging Scenarios](https://mlsanity.com/paper/2309.13206/) (2023)
- [Probabilistic Human Intent Prediction for Mobile Manipulation: An Evaluation with Human-Inspired Constraints](https://mlsanity.com/paper/2507.10131/) (2025)
- [MANER: Multi-Agent Neural Rearrangement Planning of Objects in Cluttered Environments](https://mlsanity.com/paper/2306.06543/) (2023)
- [Push Smarter, Not Harder: Hierarchical RL-Diffusion Policy for Efficient Nonprehensile Manipulation](https://mlsanity.com/paper/2512.10099/) (2025)

## Abstract (arXiv)

The ability to communicate intention enables decentralized multi-agent robots to collaborate while performing physical tasks. In this work, we present spatial intention maps, a new intention representation for multi-agent vision-based deep reinforcement learning that improves coordination between decentralized mobile manipulators. In this representation, each agent's intention is provided to other agents, and rendered into an overhead 2D map aligned with visual observations. This synergizes with the recently proposed spatial action maps framework, in which state and action representations are spatially aligned, providing inductive biases that encourage emergent cooperative behaviors requiring spatial coordination, such as passing objects to each other or avoiding collisions. Experiments across a variety of multi-agent environments, including heterogeneous robot teams with different abilities (lifting, pushing, or throwing), show that incorporating spatial intention maps improves performance for different mobile manipulation tasks while significantly enhancing cooperative behaviors.